Alex Lotsu’s Post

The credentials to an organization's systems and data are prime targets for malicious actors.Traditional secret detetions that are used to detect credentials in code within developer workflows are limited by regex capabilities and can’t leverage any form of data-flow analysis for detecting secrets. To overcome this, one of my secrets in code analysis is manual semantic analysis, not just regex. Well, Semgrep added this to their workflow. Semgrep Secrets can reason about how data is used within your code. Its awesome you can try it. 

Need to explore how useful it is,if this can be alternative to DSL for dev tools or can be used in painless scripting.This would help many folks struggling with JSON query syntax😄

🧲 𝗥𝗲𝗴𝘂𝗹𝗮𝗿 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀 (𝗥𝗲𝗴𝗲𝘅) Regular expressions, or regex, might seem daunting at first glance, but they're an incredibly powerful tool for searching, parsing, and manipulating text data. Let's decode the basics: 🔍 𝙒𝙝𝙖𝙩 𝙞𝙨 𝙧𝙚𝙜𝙚𝙭? Regex is a sequence of characters that forms a search pattern. It can be used for 'find' or 'find and replace' operations, syntax validation, and more. 💡 𝘾𝙤𝙢𝙢𝙤𝙣 𝙍𝙚𝙜𝙚𝙭 𝙎𝙮𝙣𝙩𝙖𝙭: 🔹.: Matches any character except newline 🔹``: Matches 0 or more repetitions of the preceding character 🔹+: Matches 1 or more repetitions of the preceding character 🔹?: Matches 0 or 1 repetition of the preceding character 🔹[abc]: Matches any of the characters enclosed in the brackets 🔹[^abc]: Matches any character NOT enclosed in the brackets (abc|def): Matches abc or def 🎯 𝘼𝙥𝙥𝙡𝙞𝙘𝙖𝙩𝙞𝙤𝙣𝙨 𝙤𝙛 𝙧𝙚𝙜𝙚𝙭: 🔸 Extracting emails or URLs from a block of text 🔸 Validating input formats (like phone numbers, emails) 🔸 Parsing logs or large data files 🔸 Code refactoring Mastering regex can save you hours of manual search and manipulation, and bring your data manipulation skills to the next level. #regex #data #linux #techdeepdive — 📌 Follow me on LinkedIn and Subscribe to my newsletter for more detailed articles on System Design, DevOps, and Software Engineering (see comments 👇🏽)

2 days ago, Elastic presented her new query language - ES|QL(ElasticSearch Query Language). This query language will allow a single, consolidated way to interact with Elasticsearch — one that brings comprehensive compute capabilities close to the data and eliminates the need of expensive transfers to external systems for custom processing. In one of the examples that can be found below, we can see that in single query preform filtering, processing, grouping, renaming, sorting, look-ups and column pruning. ES|QL brings a new execution engine designed with performance in mind — one that operates on blocks at a time instead of per row, targets vectorization and cache locality, and embraces specialization and multi-threading. feel free to contact us for more info about this game changer 🎯 Ofir Adi Idan Moshe Arthur Gimpel Ido Friedman Nick Van Dyk Lior Halfin Eran Frid Miki Levinshtain Meytal Balas Elad Shalvi Dror Mor ☁️Leah Brand Zeev Shavit Sagi Kelner Max Totney KPMG Israel

🔄 Natural Language => LLM => SQL 🔄 One of the most prevalent uses of LLMs in production among our prospects at Prompt Security nowadays is translating natural language into SQL. This awesome tutorial demonstrates how to create a natural language to SQL code generator using LLM in a few minutes. With the OpenAI API, coupled with a touch of Pandas and DuckDB, it's remarkable how straightforward it is to build - producing consistently accurate results most of the time. You can access the notebook here: https://lnkd.in/d3Ja_KkQ 📓 The entire repository is available here: https://lnkd.in/dUkf386S 📁 Upcoming on their roadmap, they plan to integrate support for open-source LLMs such as Llama2 and Huggingface Transformers 🤗 Huge thanks to my incredible colleague, Rami Krispin, who is the driving force behind this project 👏👨💻.

The ability to ask Observe's O11y Extract to write regexes for you is a game changer in terms of productivity (and your sanity). https://lnkd.in/eAKnHv4Q

Effortless Base64 Encoding & Decoding. SmartToolsAI.com's Base64 Encode Decode tool is the perfect solution for swiftly converting data using the Base64 encoding scheme. Base64 encoding simplifies data transfer and binary storage, making it easily readable and transferable. Simple and User-Friendly: 1. Encode: Enter text or upload a file for encoding 2. Click "Encode" 3. Get your Base64-encoded result ⬅️ OR ➡️ 1. Decode: Input encoded text or upload a file for decoding 2. Click "Decode" 3. Watch your Base64 data restored to its original form Perfect for Developers and webmasters: Save time and effort with our easy-to-use tool, catering to those handling data encoding and decoding tasks. Your data's security and privacy are our top priority. Your input is handled with the strictest confidentiality. Effortlessly handle Base64 data with Smart Tools AI's Base64 Encode Decode tool. Simplify data tasks, securely transfer data, and elevate your workflow. Explore Now: https://lnkd.in/gyrw3Mnw #Base64 #DataEncoding #SmartToolsAI

#365DaysOfCoding Hey #LinkedInConnections, today I'm excited to dive into the fascinating world of Trie data structures. 🌐✨ Trie, pronounced as "try," stands for retrieval and is an efficient tree-like data structure that excels at handling and searching for strings. 📚🔍 Whether you're building a search engine, autocomplete feature, or spell checker, Tries can be your go-to solution. 🔑 Key Features: Efficient Searching: Tries are designed to make searching for strings lightning-fast. By breaking down words into their individual characters and storing them hierarchically, Tries dramatically reduce search time. Autocomplete and Suggestions: Ever wondered how your keyboard suggests words while typing? Tries are the magic behind this feature, providing real-time suggestions as you type. Space Efficiency: Tries are memory-efficient when dealing with a large number of words that share common prefixes. Traditional search structures like hash tables or binary search trees can't match this space-saving ability. Prefix Matching: Tries are perfect for tasks that involve finding all strings with a common prefix, such as IP routing or dictionary lookups. Insertion and Deletion: Tries can easily handle dynamic datasets. Insertions and deletions are efficient, making them suitable for applications with changing content. 🚀 Real-World Applications: Search Engines: Google's search engine harnesses the power of Tries to deliver instant search results as you type, even for complex queries. Spell Checkers: Tries enable spell checkers to quickly suggest correct spellings or alternatives, enhancing user experience. Phone Book Apps: When you search for a contact by typing the first few letters, you're benefiting from Tries' prefix matching capabilities. IP Routing: Routers use Tries to efficiently match network prefixes and determine the best path for data packets. 🔗 Let's Connect the Dots: Trie data structures might appear complex at first glance, but they offer a brilliant solution to various real-world challenges. By implementing Tries in your projects, you can unlock the potential for lightning-fast searches, autocomplete features, and more. 🚀 Have you had experience working with Tries? Or are you intrigued to explore their applications? Let's chat! Comment below or connect with me to exchange thoughts and ideas. 🤝🗣️ #TrieDataStructure #DataStructures #CodingMagic #SearchAlgorithms #TechInnovation

API Response time is critical for User Experience :)